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Research on futures trend trading
strategy based on short term chart
pattern
a
Saulius Masteika & Aleksandras Vytautas Rutkauskas
a
a
Faculty of Business Management, Department of Finance
Engineering, Vilnius Gediminas Technical University, Saulėtekio
al. 11, LT, 10223, Vilnius, Lithuania
Version of record first published: 04 Oct 2012.
To cite this article: Saulius Masteika & Aleksandras Vytautas Rutkauskas (2012): Research on
futures trend trading strategy based on short term chart pattern, Journal of Business Economics
and Management, 13:5, 915-930
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Journal of Business Economics and Management
ISSN 1611-1699 print / ISSN 2029-4433 online
2012 Volume 13(5): 915–930
doi:10.3846/16111699.2012.705252
RESEARCH ON FUTURES TREND TRADING STRATEGY
BASED ON SHORT TERM CHART PATTERN
Downloaded by [University of Windsor] at 02:10 09 November 2012
Saulius Masteika1, Aleksandras Vytautas Rutkauskas2
Faculty of Business Management, Department of Finance Engineering,
Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania
E-mails: 1saulius.masteika@vgtu.lt (corresponding author);
2aleksandras.rutkauskas@vgtu.lt
Received 16 February 2012; accepted 19 June 2012
Abstract. The main task of this paper is to examine a short term trend trading strategy in
futures market based on chart pattern recognition, time series and computational analysis.
Specifications of historical data for technical analysis and equations for futures profitability calculations together with position size measurement are also discussed in the
paper. A contribution of this paper lies in a novel chart pattern related to fractal formation and chaos theory and its application to short term up-trend trading. Trading strategy
was tested with historical data of the most active futures contracts. The results have
given significantly better and stable returns compared to the change of market benchmark
(CRB index). The results of experimental research related to the size of trading portfolio
and trade execution slippage are also discussed in the paper. The proposed strategy can
be attractive for futures market participants and be applied as a decision support tool in
technical analysis.
Keywords: futures market, quantitative analysis, trend trading, time series analysis, chart
pattern, fractal formation, technical analysis.
Reference to this paper should be made as follows: Masteika, S.; Rutkauskas, A. V. 2012.
Research on futures trend trading strategy based on short term chart pattern, Journal of
Business Economics and Management 13(5): 915–930.
JEL Classification: G15, G17, F37.
1. Introduction
In recent years the application of trading algorithms and computational analysis in financial markets has attracted attention of many scientists and researchers. The interest
in high frequency trading, online trading algorithms and time series analysis shows an
increasing belief that new computation techniques can be helpful for decision making
processes in financial markets (Schmidt, Mohr et al. 2010; Izumi, Toriumi et al. 2009;
Dempster, Lemmans 2006; Ehlers 2004; Koutroumanidis, Ioannou et al. 2011). Because
of raising uncertainty in financial markets (Belinskaja, Galiniene 2010; Girdzijauskas,
Streimikiene 2010) and growing demand for hedging businesses (Stepien 2009) trading
volume from equity and currency markets increasingly move to derivatives markets
Copyright © 2012 Vilnius Gediminas Technical University (VGTU) Press Technika
http://www.tandfonline.com/TBEM
S. Masteika, A. V. Rutkauskas. Research on futures trend trading strategy based on short term chart pattern
Downloaded by [University of Windsor] at 02:10 09 November 2012
(Acworth 2011). The need for computational analysis of futures markets is in a great
demand.
A well-known key concept is being often discussed when analysing financial markets:
the Efficient Market Hypothesis (EMH). EMH asserts that the market is efficient and
whenever new information comes up the market absorbs it by correcting itself (Mockus,
Raudys 2010). Prices reflect all publicly available information and analysis of historical
information is unfruitful. Therefore according to the Efficient Market Hypothesis it is
impossible to get better returns in the market than the market index itself (e.g. S&P500
index for stocks or CRB index for commodities). There are a lot of well-known publications that partly confirm this hypothesis (Uri, Jones 1990; Kan, O’Callaghan 2007;
McCauley 2008). However there are also published reports with opposite statement,
that EM hypothesis is far from the truth (Warwick 1996; Jarret, Schilling 2008; Simutis,
Masteika 2006; Atsalakis, Valavanis 2009; Lee 2010) and show weak-form efficiency of
economies (Nurunnabi 2012). The fact that some market participants (high frequency
traders or hedge funds) can consistently beat the market is a good indication that the
EM hypothesis may not be just right. For scientists it is difficult to prove the real profitability of high frequency traders. Usually the information about profitable algorithms is
kept secret and not published by practitioners. Despite the shortage of hard evidences
about the profitability of trading algorithms there are some research papers claiming
that the application of momentum trend trading can give some useful information for
market participants (Miffre, Rallis 2007; Szakmary 2010; Harris, Yilmaz 2009). Strategies based on chart patterns and momentum trend following techniques can beat the
market (Friesen, Weller et al. 2009; Leigh, Paz et al. 2002; Lee, Oh et al. 2012; Williams, B. M., Williams, J. G. 2004). In this paper we try to reinforce these statements.
A contribution of this paper lies in a modified chart pattern related to fractal formation
and chaos theory and its application to short term up-trend trading strategy. The paper
presents the details of making computational analysis and applying ranking techniques
in futures markets. The proposed short term trend trading strategy based on chart pattern
might be interesting for futures market participants and hedge funds. Trading strategy
was tested with historical data of the most active futures contracts in USA markets.
The results clearly contradict statements of Efficient Market Hypothesis and give significantly more stable and better returns if compared to the change of market average
(CRB index). The paper is organized as follows: Section 2 provides the basic concept of
the proposed short term trading strategy. Section 3 explains the details of experimental
investigations. Experimental results are evaluated in section 4 and the main conclusions
of the research are presented in Section 5.
2. Basic concept of short term trend trading strategy
Short term trend trading strategy is based on up-trend following system and continuous
chart pattern. Chart pattern is an indicator of technical analysis mostly applied for interpretation and market forecast (Liu, Kwong 2007). There are two types of patterns in
technical analysis, reversal and continuation (Bulkowski 2005; Kirkpatrick II, Dahlquist
2010). A reversal pattern signals that a prior trend will reverse upon completion of the
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Journal of Business Economics and Management, 2012, 13(5): 915–930
pattern. A continuation pattern signals that a trend will continue once the pattern is
triggered.
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The proposed continuous chart pattern relates to a fractal pattern and chaos theory,
broadly discussed by Benoit B. Mandelbrot (Mandelbrot 2006; Mandelbrot et al. 2010)
and developed as a trading tool by Ph.D. Bill M. Williams (Williams, B. M., Williams, J. G. 2004). Fractal formation is built from chart bars. Fractal helps to determine
a possibility of further continuous movement in the market. The following figure shows
some fractal formations.
A fractal definition presented by B. M. Williams is as follows: “A Fractal must have two
preceding and two following bars with lower highs (higher lows in a down move). In a
buy Fractal, we are interested only in the bars’ high. In a sell Fractal, we are interests
only in the bars’ low” (Williams, B. M., Williams, J. G. 2004).
As we can see from examples in Fig. 1
variety of fractal patterns can be unlimited as long as there are two preceding
and two following chart bars with lower
Fig. 1. A few examples of up-trend fractal
highs. Time horizon for fractal to make a
formations
formation is inaccurate. Uncertainty also
increases because of no clear rules for stop
or market exit orders are present. Maybe therefore fractals are often suggested to use in
conjunction with other forms of technical analysis, like moving averages, Elliott Waves
analysis or MACD indicator (Williams 1998). These shortages together with a lack of
fractal signal strength measurement constitute inaccuracies when trying to build a computerized trading system. The easiest way to overcome these problems is to do back test
analysis with only a pre-selected particular contract. But such a way makes quantitative
analysis limited and inaccurate to ever changing financial instruments and behavior of
the market. Generalizing, classical fractals might be as a good decision support tool, but
not as a basic indicator itself. Therefore a decision to search for a novel more accurate,
determined and at the same time still conformable with trend trading and chaos theory
chart pattern was made.
2.1. Chart pattern for trading strategy
Proposed short term chart pattern is composed from 3 consecutive chart bars and uses
only the latest data for decision making. A fractal occurs when there is a pattern with
the highest high in the middle and one lower high on each side, as it can be seen in
some examples in Fig. 2.
Trading strategy opens a long position when current price (i) tops the second bar’s (i–1)
highest price. An open price is set as the second bar’s (i–1) high level plus a tick size.
Tick size is a minimal price increment. Each contract has a different tick size because
of the contract size. Tick size can have a decimal or fractional number format. Most
of the time decimal number format is used because of standardization of data sets, e.g.
CSI data format. If current price bar (i) opens higher than (i–1) and forms a gap, a buy
signal is identified as an opening price.
917
S. Masteika, A. V. Rutkauskas. Research on futures trend trading strategy based on short term chart pattern
Buy
Buy
i
i -2
i-1
Trailing
Stop
i-1 i
i -2
i-3
i -4
Trailing
Stop
i -4
i -3
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Fig. 2. Examples of proposed chart pattern
Deciding the appropriate time to close the position is just as important as determining
the best time to open it. Our strategy suggests that a trailing stop order should be set at
the level of the second bars (i–1) lowest price minus a tick size. The trailing stop price
is adjusted and moved to the next bar lowest price each time the new bar is formed. This
lets profits run and cut losses in response in to the market price changes. For example,
if the price rises, the trailing stop level rises as well. And if a price movement reverses
the trailing stop closes the position. If there is a sudden crash in the market and next
bar opens with a gap down, sell price is set as an open price of this following bar. It
should also be noted that the opening and closing of the position cannot be fulfilled on
the time frame of the same bar.
2.2. Signal strength and contracts ranking
Most of the time back-test analysis is implemented only with historical data from preselected particular contract. This approach can be applied but is static, subjective and
lacks flexibility to all the time changing instruments and their behavior in the markets
(trendy market is changed by consolidated, reversal, choppy and etc.). Research results can often be predetermined in advance by taking only a particular contract or the
most suitable time period for analysis. In order to avoid such subjectivity when doing
quantitative analysis ranking techniques of contracts and the quality of signals must be
considered (Masteika 2010).
Proposed short term trading strategy is searching for futures contracts with the biggest
two day’s price increase where the lowest price of the last bar is higher than the one a
month ago, i.e. the lowest price of the period i is greater than the lowest price of the
period i–20. The quality of the contract and its rank for the trade is measured using the
following equation:
C
R  i  2 100  100.
(1)
Ci  4
In the equation (1) R represents the rank of a particular contract and C- the closing price
of the period i. The biggest increase in price of a particular contract during the period
between i–4 and i–2 also means the best quality and the highest rank. If the chart pattern is formed with several contracts at a time, the one with the highest rank is chosen
for a trade.
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Journal of Business Economics and Management, 2012, 13(5): 915–930
3. Experimental research
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The efficiency of the proposed short term trend trading strategy was tested with historical data collected from automated electronic futures exchanges. The daily time series
were collected from exchanges like GLOBEX, ECBOT, NYMEX, CFE, ICE and ICENYBOT from 1991 till 2011. CSI UA software developed by CSI Commodity Systems
Inc. was chosen as the primary instrument collecting the data.
The most active and liquid contracts from each sector of futures market were included
in experimental research. In general, futures are grouped into following sectors: Energies, Metals, Grains, Financials (Interest rates), Indices, Currencies, Softs and Meats
(Kleinman 2004; Lambert 2010). These sectors are often visualized as Heat Maps.
Fig. 3 presents an example of such a Heat map in futures industry.
Heat map or sector analysis is applied when classifying contracts and implementing
quantitative analysis based on ranking techniques. Table 1 presents the most active
contracts in each sector.
Table 1. The most active contracts in the sector
Energies
Metals
Grains
Financials
• WTI Crude Oil
(1000 barrels)
• Henry Hub Natural
Gas (10000 mil.
BTU)
• Heating Oil #2
(42000 gallons)
• Gold (100 ounces)
• Silver (5000 ounces)
• Copper (25000
pounds)
• Platinum (50 troy
ounces)
• Corn (5000 bushels)
• Soybean
(5000 bushels)
• Wheat (5000
bushels)
• Soybean Oil
(60000 pounds)
• Soybean Meal
(100 short tons)
• Eurodollar (1 mln. $)
• 10 Year Treasury
Note (100000$)
• 30 Year Treasury
Bond (100000$)
• 2 Year Treasury Note
(200000$)
Indices
Currencies
Softs
Meats
• E-mini S&P500
(50$)
• E-mini Nasdaq
100 (20$)
• DJIA Index Mini
(5$)
• Russell 2000 mini
(100$)
• Volatility Index,
VIX (1000$)
• Euro FX
(125000 Euro)
• Japanese Yen
(12500000 Yen)
• British Pound
(62500 GBP)
• Australian Dollar
(100000 AUD)
• Canadian dollar
(100000 CAD)
• Sugar #11
(50 long tons)
• Cotton #2 (50000
pounds)
• Orange Juice
(15000 pounds)
• Coffee
(37500 pounds)
• Lumber (110 board
feet ~ 260 cubic
meters)
• Live Cattle
(40000 pounds)
• Lean Hogs
(40000 pounds)
• Class III Milk
(200000 lbs.
~90 metric tons)
• Feeder Cattle
(50000 pounds)
Table 1 was built considering futures trading volume analysis based on statistical data
from 2010 annual survey (Acworth 2011); CRB statistics (Commodity Research Bureau
2010) and Barchart.com, Inc on-line data. Numbers next to each contract in the brackets
show an index multiplier for indices and contract size for other futures. Futures in Table
1 are the most liquid and all together take the biggest part of trading activity in the North
American futures markets, therefore were chosen for back test analysis.
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Barchart Futures Market Heat Map
Interest
Rates
+25%
Energies
+0.18%
Softs
+0.17%
Metals
+0.16%
All Markets
+0.07%
Grains
+0.06%
Currencies
0.02%
Meats
0.02%
Equity
Indices
0.27%
27%
0
+25%
Fig. 3. A Heat Map example (Source: Barchart.com, Inc.)
3.1. Continuous futures data series
Futures historical data faces different problems with data gaps if compared to stock
or Forex markets. The data set must be specified in the first place when doing computational calculations and back test analysis (Rutkauskas, Ramanauskas 2009). The
differences appear because of short-lived contracts. Futures contracts are standardized
contract between two parties to exchange a specified asset of standardized quantity
and quality for a price agreed today (the futures price) but with delivery occurring at a
specified future date (delivery date) (Kolb, Overdahl 2006). For example the most active
contract for DowJones Index Mini_Sized (DJIA) in July, 2011 is YMU11, i.e. Sept. 11.
Around mid-September most activity will roll over to the December contract YMZ11
and afterwards the YMU11 contract will expire. The problem with historical data series
arises trying to calculate long-term profitability of trading strategies based on quantitative analysis. There are several methods to generate continuous futures data series, like
Forward adjusted; proportionally adjusted; Gann series; perpetual series or backward
adjusted series (Masteika et al. 2012). Taking into account the strengths and weaknesses
of different methods backward adjusted data series were taken in experimental research.
Backward adjusted data uses actual prices of the most recent contract with a backward
correction of price discontinuities for successive earlier active delivery months. This
adjustment requires applying recalculation of the earlier contract prices with respect to
the price of the current (or later) contract (Pelletier 2011). After applying the method of
backward data adjustment the problem with negative prices was resolved by adding a
fixed amount of points to make non-negative values. This fix of the data was necessary
only with Soybean meal contract.
A roll over timing. All methods for making continuous data series are depended on
evaluation of particular place where the contracts should be switched from one to an920
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Journal of Business Economics and Management, 2012, 13(5): 915–930
other. Roll timing determines when the near contract is dropped and replaced by the next
one. Roll timing depends upon either a given calendar date, magnitude of volume, open
interest or both volume and open interest (Pelletier 2011). When generating continuous data series the simplest way is to use a calendar date for a roll over, for example
the day before contract’s expiration date. But when expiration date approaches open
interest and so trading volume decreases and liquidity also lowers. In real trading low
liquidity associates with bigger spreads between bid and ask prices. Bigger spreads
impact trade execution slippage, especially with larger transactions. In order to make
qualitative quantitative analysis with minimum distortions and smallest commissions
when changing the contracts a roll over timing was linked to open interest and volume.
In experimental research confirmation signal (roll on a second consecutive trigger) also
was applied because of possible sudden but not approved increase in volume or open
interest. The methods chosen have allowed historical data to roll on between futures
contracts on the most liquid trading days with the lowest risk of execution slippage.
3.2. Contracts specifications
Contracts specifications necessary for calculations are detailed in this section. The main
inadequacies between back testing futures and stocks or currencies appear because of
different margin requirements, full point values and tick sizes when trading derivatives.
For example one percent change in a price of Soybean Oil contract generates different
percentage change in account value if compared with 1% change in a price of DJIA
Index Mini contract. As we can see from the following table OIMR (Overnight Initial
margin requirements) and Full Point values as well as Tick sizes and commissions for
each contract differ.
Table 2 shows specifications of futures applied for risk, profitability and position size
calculations when back testing the strategy. For example Full point value and OIMR
were applied when calculating position sizes and risks. Exchanges calculate Full Point
Value considering the size of a contract, unit of measuring, tick size, market fluctuations
and the total value of the contract. Margin requirements are being mostly expressed in
currency of the traded product and based on SPAN margin algorithm. The SPAN margin algorithm defines a standard set of market outcome scenarios with a one day time
horizon. In our research OIMR values presented in Table 2 were taken from Interactive
Brokers LLC data base on the 25th of July, 2011. OIMR can be also explained as a
margin deposit for end of day trading. Tick size was necessary defining exact price for
opening and closing positions. Currency and commissions were applied evaluating the
costs of making a trade with different contracts. Commission charges presented in Table
2 have a fixed rate per contract and include all the fees from exchanges and regulatory
institutions. The charges are based on Interactive Brokers LLC commission table structure. According to the surveys conducted by elitetrader.com Interactive Brokers LLC is
a leader of direct access brokers allowing to trade in various markets (stocks, options,
futures, ETFs, FOREX, Funds, Bonds, CFDs and etc.).
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S. Masteika, A. V. Rutkauskas. Research on futures trend trading strategy based on short term chart pattern
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Table 2. Contracts specifications
Symbol
Exchange
Name
Tick size
Full Point
value
OIMR
Commissions
CL
NYMEX
WTI Crude Oil
0.01
1000
8775
$2.32
NG
NYMEX
Henry Hub Natural Gas
0.001
10000
3713
$2.32
HO
NYMEX
Heating Oil #2
0.0001
42000
9534
$2.32
GC
NYMEX
Gold
0.1
100
6075
$2.32
SI
NYMEX
Silver
0.1
50
25920
$2.32
HG
NYMEX
Copper
0.05
250
5852
$2.32
PL
NYMEX
Platinum
0.1
50
4375
$2.32
ZC
ECBOT
Corn
0.25
50
3260
$2.68
ZS
ECBOT
Soybean
0.25
50
4388
$2.68
ZW
ECBOT
Wheat
0.25
50
4374
$2.68
ZL
ECBOT
Soybean oil
0.01
600
1688
$2.68
ZM
ECBOT
Soybean meal
0.1
100
2363
$2.68
GE
GLOBEX
Eurodollar
0.0025
2500
608
$2.06
ZN
ECBOT
10 Year Treasury Note
0.015625
1000
2049
$1.43
ZB
ECBOT
30 Year Treasury Note
0.015625
1000
3078
$1.43
ZT
ECBOT
2 Year Treasury Note
0.0078125
2000
675
$1.43
ES
GLOBEX
E-mini S&P500
0.25
50
5000
$2.01
NQ
GLOBEX
E-mini Nasdaq 100
0.25
20
3500
$2.01
YM
ECBOT
DJIA Index Mini
1
5
5000
$2.01
TF
ICE
Russell 2000 mini
0.05
100
4375
$2.01
VIX
CFE
Volatility Index
0.05
1000
4000
$1.87
EUR
GLOBEX
Euro FX
0.0001
125000
5616
$2.47
JPY
GLOBEX
Japanese Yen
0.0001
125000
3375
$2.47
GBP
GLOBEX
British Pound
0.0001
62500
2147
$2.47
AUD
GLOBEX
Australian Dollar
0.0001
100000
3213
$2.47
CAD
GLOBEX
Canadian dollar
0.0001
100000
2484
$2.47
SB
ICE_NYBOT
Sugar #11
0.01
1120
3557
$2.62
CT
ICE_NYBOT
Cotton #2
0.01
500
8400
$2.62
OJ
ICE_NYBOT
Orange Juice
0.05
150
1798
$2.62
KC
ICE_NYBOT
Coffee
0.05
375
7787
$2.62
LB
GLOBEX
Lumber
0.1
110
2745
$2,76
LE
GLOBEX
Live Cattle
0.025
400
2106
$2.76
HE
GLOBEX
Lean Hogs
0.025
400
2160
$2.76
GF
GLOBEX
Feeder Cattle
0.025
500
3119
$2.76
DA
GLOBEX
Class III Milk
0.01
2000
675
$2,76
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3.3. Profit/loss and position size calculations
One of the most difficult tasks trading and back testing futures markets is to understand
how each contract is quoted, what the full point value or multiplier of each contract
is and also how to calculate profit, loss or position size of a trade. In order to make a
trade margin deposit must be considered in the first place. Margin deposit is paid for
each contract when opening position on future contract. The margin is a little part of
a full contract value. For example with a margin of 7% around 14 contracts can be
acquired paying no more than a full value of one contract. This gives a leverage that
makes trading futures more risky and also profitable. The simplest way to make calculations is with a contract where the full point value equals to 1. Then each $1 change in
contract price represents $1 profit/loss per contract. But most of the time futures have
point value different than 1, see Table 2: Full Point values. If, for example, point value
is 5, as trading with DJIA Index mini, each point change in a price of the contract represents $5 profit/loss in equity. When position is closed the margin is back deposited
and profit/loss calculated. Profit/loss is calculated multiplying the number of contracts
by full point value and by the difference between sell and buy prices. The successive
equation presents the profit/loss calculations used in computational analysis taking also
into consideration commission charges and interest on margin paid:
P / L  N  (( Pc  Po )  FPVi  2  Comm)  Int.
(2)
In the equation (2) Pc and Po represents closing and opening prices of a particular
contract. FPV shows the Full Point value of a particular contract i (see: Table 2). The
variable Comm shows the value of commissions for a trade. The variable Comm is multiplied by 2 because there is a commission charge both for opening and closing order.
The variable N represents the number of contracts and Int shows the value of interest
paid on margin.
Successful futures market traders risk as little of their money, as possible. It is common
in the industry that larger capitalized futures traders tend to risk rarely more than 1%
per trade. Smaller size traders let’s say with an account smaller than 250k tend to risk
no more than 2–5% of their total equity per trade (Schwager, Jack 2008). Let’s take an
example and say that initial equity is set to $200’000 and investor wants to invest up to
5% of equity per single trade and margin deposit (OIMR) is $5’000 (as with DJIA Index
mini at 25/07/11). Therefore investor’s appropriate position size should be $200’000 *
5% = $10’000. Which means that trading DJIA Index mini contract with such an amount
of money no more than 2 contracts can be purchased per trade ($10’000/$5’000 = 2).
The following equation is applied doing experimental research when evaluating the size
of a single trade:
Rr
Ie 
 2  Comm
100
.
PS 
(3)
OIMRi
In the equation (3) the calculation of position size – PS is presented. The variable Ie
shows the size of initial capital and Rr (Risk ratio) – the percentage size of risk taken
if compared to total equity. Rr depends on the size of initial equity and also on the for923
S. Masteika, A. V. Rutkauskas. Research on futures trend trading strategy based on short term chart pattern
wardness toward risk taking. For example if a trader can afford to risk a half of total equity on a single trade Rr coefficient equals to 50. The variable Comm shows the amount
of commissions required for a trade. OIMR is overnight initial margin requirements for
opening position with a particular contract i. OIMR values (see Table 2) can change
accordingly to market volatility and should be rechecked when opening new positions.
4. Experimental results
Trading strategy based on short term chart pattern was back-tested using initial account
balance of 1 million U.S. dollars (USD). Overall the charts in Fig. 4 show positive results. Trading strategy had been back tested applying several different risk ratios while
the initial account balance and other parameters were left unchanged. As it was stated
earlier in the paper conservative approach says that only 1 or 2 percent of total account
balance should be risked per trade. Less conservative investors tend to risk larger sums.
As Fig. 4 shows a small change in risk ratio generates a prominently different outcome.
When risk ratio is 1 percent, account balance is almost multiplied by 4 at the end of
testing period. However, when risk ratio is 1.25%, account balance stops at 6 million
USD. When the risk ratio is at 1.5%, account balance increases to over 8 million USD.
Additionally, the profitability still has room to grow considering the possibility to use
a bigger risk ratio per trade. For example smaller funds accept the rule to trade with
3–5% of the total capital on one position, not mentioning private investors trading over
20 or even 50% of the total capital per trade. It is clear that the greater the risk ratio the
bigger account balance fluctuations can be.
×106
9
8
Rr = 1%
Rr = 1.25%
Rr = 1.5%
Account balance ($)
7
6
5
4
3
2
1
Date
Fig. 4. Research results with different risk ratios
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The strategy was back tested and calculations were carried out applying software of
technical computing- MatLab 2010, developed by The MathWorks, Inc. Experimental
results of the strategy are presented in the following Figures.
Journal of Business Economics and Management, 2012, 13(5): 915–930
The results of trading strategy and account balance fluctuations have been also compared
to the change of market average (CRB index). CRB Index is often applied as a track of
collective trend changes of the commodities markets. As we can see in Fig. 5 the chart
of account balance, applying 1.5% risk ratio, gives more stable results if compared to
CRB Index changes. Financial crisis and the market crash, when CRB Index was tumbling nearly 50%, hadn’t impacted a lot the overall performance of the strategy. This
chart shows that the strategy is effective and immune to various market fluctuations.
The chart lines in Fig. 6 show difference between the results trading different number
of futures. For example ‘1 contract’ in Fig. 6a, b means that only one future was traded
at a time, no matter if it was a commodity, stock index, currency, financials or etc. ‘2
contracts’ means that only two open positions of different futures with the highest rank
were in portfolio; ‘3 contracts’- only three different positions possible and so on. As
we can see in Fig. 6a, all experiments at the end of the period show positive results.
The best results were received trading 15 and 20 different contracts at a time. It can be
simply explained by a bigger amount of capital used. Nevertheless this research and
the charts in Fig. 6b show that it is less risky to start applying the strategy with greater
diversification, i.e. from 8 futures and above. Zoomed research and Fig. 6b shows that
bigger diversification gives steadier increase in total balance and visa versus the diversification of up till 5 futures generates quite plane and even down trend results. In general,
an experiment showed that portfolio diversification is necessary with this strategy and
15 futures is optimal amount for this data set.
Considering that trading costs consist of commissions and also possible trade execution
slippage additional research on the impact of trade execution slippage was done. The
results of the research are graphically presented in the following figure.
×106
800
8
700
Rr = 1.5%
CRB Index
6
600
4
500
2
400
0
300
CRB Index (Index value)
10
Account balance ($)
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The results presented in Figs. 4 and 5 were achieved trading 15 different futures with the
highest rank at a time. Additional research showed that the number of different futures
traded at a time plays a huge role on overall performance of the strategy, see Fig. 6.
Fig. 5. Account balance and CRB index change
925
S. Masteika, A. V. Rutkauskas. Research on futures trend trading strategy based on short term chart pattern
×106
10
a)
9
1 contract
2 contract
3 contract
5 contract
8 contract
15 contract
20 contract
8
Account balance ($)
7
6
5
4
3
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2
23/02/04
19/11/06
15/08/09
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28/09/93
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21/05/95
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1
Date
6
1.6
b)
×10
1 contract
2 contract
3 contract
5 contract
8 contract
15 contract
20 contract
1.5
Account balance ($)
1.4
1.3
1.2
1.1
12/03/93
24/08/92
06/02/92
02/01/91
0.9
21/07/91
1
Date
Fig. 6. Impact of portfolio diversification on the results
The chart lines in Fig. 7a show an impact of trade execution slippage on total account
balance. The impact of slippage is obvious and even influences overall profitability of
the strategy. As for example if there was a constant 8 tick’s slippage per trade account
would turn to negative. Since calculations were done with Rr ratio of 1.5% it was interesting to see if bigger risk ratios could compensate the negative effect of the slippage.
The research has shown that up till 5 ticks the negative effect can be compensated by
taking bigger risks, e.g. Rr = 3%; 5% or even higher. But starting from size of 6 ticks an
increase in risk ratio doesn’t help and even worsens situation. As we can see in Fig. 7b
with execution slippage of 8 ticks bigger Rr ratios gives only lower values of account
926
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×106
9
a)
8
Account balance ($)
7
1 tick slippage
3 tick slippage
5 tick slippage
8 tick slippage
6
5
4
3
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19/11/06
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b)
×106
4
Account balance ($)
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2
3
2
1
0
5
4
Rr
8
3
rat
i
o(
%)
2
1 5
7
6.5
6
(Ticks)
e
g
a
p
5.5
on slip
Executi
7.5
Fig. 7. Impact of trade execution slippage on the results
balance. This show that the proposed strategy can be profitable only when the size of
the slippage is small one, i.e. less than 5 ticks. The problem with slippage is important
therefore such technologies as high frequency trading (HFT) or special order types like
Iceberg, Hidden, Fill or Kill (FOK), Immediate or Cancel (IOC), Limit if Touched (LIT)
and etc. should be considered when using this strategy.
5. Conclusions
Recent series of volume records and new all-time high trading activity in futures market
were the main driving forces for this research. The main goal of this paper was to present the strategy and quantitative research results based on short term chart pattern and
technical analysis. The paper also reviewed the most active automated North American
927
S. Masteika, A. V. Rutkauskas. Research on futures trend trading strategy based on short term chart pattern
futures exchanges. General futures contract grouping and the most active contracts in
each group were filtered as well. Specifications of contracts necessary for computational
and technical analysis were identified. The issues related to profitability and position
size calculations in futures markets were also discussed in this paper.
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Preliminary experimental tests have shown that the proposed chart pattern and trading strategy outperforms the market average (CRB Index). Experimental research has
showed rather stable results if compared to overall market fluctuations during back test
period. Research has proved that applying the strategy diversification and strict control
on order execution slippage are necessary.
The conclusion can be made that the proposed strategy gives some promising results
and can be attractive for hedge funds or futures market participants trading short term
strategies, especially when volatility in markets increases. Despite of good preliminary
results more extensive tests with the proposed methodology should be done (different
markets, down trend and short selling opportunities) to evaluate more precisely the
whole domain of possible applications of this strategy.
Acknowledgements
This research as Fellowship is being funded by European Union Structural Funds project “Postdoctoral Fellowship Implementation in Lithuania” within the framework of the
Measure for Enhancing Mobility of Scholars and Other Researchers and the Promotion
of Student Research (VP1-3.1-ŠMM-01) of the Program of Human Resources Development Action Plan.
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Saulius MASTEIKA. PhD is a scientific researcher of Faculty of Business Management at Vilnius
Gediminas Technical University, Lithuania. He successfully defended his dissertation thesis at Department of Informatics of Vilnius University Kaunas Faculty of Humanities. He has also received a
diploma of Social Pedagogy at Tollare High School, Stockholm. He has been teaching as a lecturer
and associated professor on the subjects related to economic modeling, computational finance and
information technologies. His research interests are related to computational and quantitative finance,
commodity markets and derivatives.
Aleksandras Vytautas RUTKAUSKAS is a professor of Faculty of Business Management at Vilnius
Gediminas Technical University, Lithuania. He is also a head of department of Finance Engineering.
Aleksandras Vytautas Rutkauskas is an expert of the board in social sciences at the Science Council of
Lithuania. His research interests are related to financial system development, accounting and auditing
theories and methodologies, decision making on economic problems of environment protection and
business risk estimations.
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